{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a notebook to generate mel-spectrograms from a TTS model to be used for WaveRNN training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TTS_PATH = \"/home/erogol/projects/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import os\n",
    "import sys\n",
    "sys.path.append(TTS_PATH)\n",
    "import torch\n",
    "import importlib\n",
    "import numpy as np\n",
    "from tqdm import tqdm as tqdm\n",
    "from torch.utils.data import DataLoader\n",
    "from TTS.datasets.TTSDataset import MyDataset\n",
    "from TTS.layers.losses import L1LossMasked\n",
    "from TTS.utils.audio import AudioProcessor\n",
    "from TTS.utils.visual import plot_spectrogram\n",
    "from TTS.utils.generic_utils import load_config, setup_model, sequence_mask\n",
    "from TTS.utils.text.symbols import make_symbols, symbols, phonemes\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES']='2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_filename(wav_path, out_path):\n",
    "    wav_file = os.path.basename(wav_path)\n",
    "    file_name = wav_file.split('.')[0]\n",
    "    os.makedirs(os.path.join(out_path, \"quant\"), exist_ok=True)\n",
    "    os.makedirs(os.path.join(out_path, \"mel\"), exist_ok=True)\n",
    "    os.makedirs(os.path.join(out_path, \"wav_gl\"), exist_ok=True)\n",
    "    wavq_path = os.path.join(out_path, \"quant\", file_name)\n",
    "    mel_path = os.path.join(out_path, \"mel\", file_name)\n",
    "    wav_path = os.path.join(out_path, \"wav_gl\", file_name)\n",
    "    return file_name, wavq_path, mel_path, wav_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "OUT_PATH = \"/data/rw/pit/data/turkish-vocoder/\"\n",
    "DATA_PATH = \"/data/rw/home/Turkish\"\n",
    "DATASET = \"ljspeech\"\n",
    "METADATA_FILE = \"metadata.txt\"\n",
    "CONFIG_PATH = \"/data/rw/pit/keep/turkish-January-08-2020_01+56AM-ca5e133/config.json\"\n",
    "MODEL_FILE = \"/data/rw/pit/keep/turkish-January-08-2020_01+56AM-ca5e133/checkpoint_255000.pth.tar\"\n",
    "BATCH_SIZE = 32\n",
    "\n",
    "QUANTIZED_WAV = False\n",
    "QUANTIZE_BIT = 9\n",
    "DRY_RUN = False   # if False, does not generate output files, only computes loss and visuals.\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "print(\" > CUDA enabled: \", use_cuda)\n",
    "\n",
    "C = load_config(CONFIG_PATH)\n",
    "ap = AudioProcessor(bits=QUANTIZE_BIT, **C.audio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
   "# if the vocabulary was passed, replace the default\n",
    "if 'characters' in C.keys():\n",
    "    symbols, phonemes = make_symbols(**C.characters)\n",
    "\n",
    "# load the model\n",
    "num_chars = len(phonemes) if C.use_phonemes else len(symbols)\n",
    "# TODO: multiple speaker\n",
    "model = setup_model(num_chars, num_speakers=0, c=C)\n",
    "checkpoint = torch.load(MODEL_FILE)\n",
    "model.load_state_dict(checkpoint['model'])\n",
    "print(checkpoint['step'])\n",
    "model.eval()\n",
    "model.decoder.set_r(checkpoint['r'])\n",
    "if use_cuda:\n",
    "    model = model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor = importlib.import_module('TTS.datasets.preprocess')\n",
    "preprocessor = getattr(preprocessor, DATASET.lower())\n",
    "meta_data = preprocessor(DATA_PATH,METADATA_FILE)\n",
    "dataset = MyDataset(checkpoint['r'], C.text_cleaner, ap, meta_data,tp=C.characters if 'characters' in C.keys() else None, use_phonemes=C.use_phonemes,  phoneme_cache_path=C.phoneme_cache_path, enable_eos_bos=C.enable_eos_bos_chars)\n",
    "loader = DataLoader(dataset, batch_size=BATCH_SIZE, num_workers=4, collate_fn=dataset.collate_fn, shuffle=False, drop_last=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate model outputs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "file_idxs = []\n",
    "metadata = []\n",
    "losses = []\n",
    "postnet_losses = []\n",
    "criterion = L1LossMasked()\n",
    "with torch.no_grad():\n",
    "    for data in tqdm(loader):\n",
    "        # setup input data\n",
    "        text_input = data[0]\n",
    "        text_lengths = data[1]\n",
    "        linear_input = data[3]\n",
    "        mel_input = data[4]\n",
    "        mel_lengths = data[5]\n",
    "        stop_targets = data[6]\n",
    "        item_idx = data[7]\n",
    "\n",
    "        # dispatch data to GPU\n",
    "        if use_cuda:\n",
    "            text_input = text_input.cuda()\n",
    "            text_lengths = text_lengths.cuda()\n",
    "            mel_input = mel_input.cuda()\n",
    "            mel_lengths = mel_lengths.cuda()\n",
    "\n",
    "        mask = sequence_mask(text_lengths)\n",
    "        mel_outputs, postnet_outputs, alignments, stop_tokens = model.forward(text_input, text_lengths, mel_input)\n",
    "        \n",
    "        # compute loss\n",
    "        loss = criterion(mel_outputs, mel_input, mel_lengths)\n",
    "        loss_postnet = criterion(postnet_outputs, mel_input, mel_lengths)\n",
    "        losses.append(loss.item())\n",
    "        postnet_losses.append(loss_postnet.item())\n",
    "\n",
    "        # compute mel specs from linear spec if model is Tacotron\n",
    "        if C.model == \"Tacotron\":\n",
    "            mel_specs = []\n",
    "            postnet_outputs = postnet_outputs.data.cpu().numpy()\n",
    "            for b in range(postnet_outputs.shape[0]):\n",
    "                postnet_output = postnet_outputs[b]\n",
    "                mel_specs.append(torch.FloatTensor(ap.out_linear_to_mel(postnet_output.T).T).cuda())\n",
    "            postnet_outputs = torch.stack(mel_specs)\n",
    "        elif C.model == \"Tacotron2\":\n",
    "            postnet_outputs = postnet_outputs.detach().cpu().numpy()\n",
    "        alignments = alignments.detach().cpu().numpy()\n",
    "\n",
    "        if not DRY_RUN:\n",
    "            for idx in range(text_input.shape[0]):\n",
    "                wav_file_path = item_idx[idx]\n",
    "                wav = ap.load_wav(wav_file_path)\n",
    "                file_name, wavq_path, mel_path, wav_path = set_filename(wav_file_path, OUT_PATH)\n",
    "                file_idxs.append(file_name)\n",
    "\n",
    "                # quantize and save wav\n",
    "                if QUANTIZED_WAV:\n",
    "                    wavq = ap.quantize(wav)\n",
    "                    np.save(wavq_path, wavq)\n",
    "\n",
    "                # save TTS mel\n",
    "                mel = postnet_outputs[idx]\n",
    "                mel_length = mel_lengths[idx]\n",
    "                mel = mel[:mel_length, :].T\n",
    "                np.save(mel_path, mel)\n",
    "\n",
    "                metadata.append([wav_file_path, mel_path])\n",
    "\n",
    "    # for wavernn\n",
    "    if not DRY_RUN:\n",
    "        pickle.dump(file_idxs, open(OUT_PATH+\"/dataset_ids.pkl\", \"wb\"))      \n",
    "    \n",
    "    # for pwgan\n",
    "    with open(os.path.join(OUT_PATH, \"metadata.txt\"), \"w\") as f:\n",
    "        for data in metadata:\n",
    "            f.write(f\"{data[0]}|{data[1]+'.npy'}\\n\")\n",
    "\n",
    "    print(np.mean(losses))\n",
    "    print(np.mean(postnet_losses))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for pwgan\n",
    "with open(os.path.join(OUT_PATH, \"metadata.txt\"), \"w\") as f:\n",
    "    for data in metadata:\n",
    "        f.write(f\"{data[0]}|{data[1]+'.npy'}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot posnet output\n",
    "idx = 1\n",
    "mel_example = postnet_outputs[idx]\n",
    "plot_spectrogram(mel_example[:mel_lengths[idx], :], ap);\n",
    "print(mel_example[:mel_lengths[1], :].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot decoder output\n",
    "mel_example = mel_outputs[idx].data.cpu().numpy()\n",
    "plot_spectrogram(mel_example[:mel_lengths[idx], :], ap);\n",
    "print(mel_example[:mel_lengths[1], :].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot GT specgrogram\n",
    "wav = ap.load_wav(item_idx[idx])\n",
    "melt = ap.melspectrogram(wav)\n",
    "print(melt.shape)\n",
    "plot_spectrogram(melt.T, ap);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# postnet, decoder diff\n",
    "from matplotlib import pylab as plt\n",
    "mel_diff = mel_outputs[idx] - postnet_outputs[idx]\n",
    "plt.figure(figsize=(16, 10))\n",
    "plt.imshow(abs(mel_diff.detach().cpu().numpy()[:mel_lengths[idx],:]).T,aspect=\"auto\", origin=\"lower\");\n",
    "plt.colorbar()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pylab as plt\n",
    "# mel = mel_poutputs[idx].detach().cpu().numpy()\n",
    "mel = postnet_outputs[idx].detach().cpu().numpy()\n",
    "mel_diff2 = melt.T - mel[:melt.shape[1]]\n",
    "plt.figure(figsize=(16, 10))\n",
    "plt.imshow(abs(mel_diff2).T,aspect=\"auto\", origin=\"lower\");\n",
    "plt.colorbar()\n",
    "plt.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
